A number of methods have been recently proposed to estimate the marginal importance weight \(\omega \)  [1]}, [2]}, [3]}.
These methods typically require solving a complex saddle-point optimization,
casting doubts on their training stability especially when combined with policy learning on continuous-control offline MBRL problems.
In this section, we mimic the Bellman backup to derive a fixed-point-style method for estimating the MIW.
